Hypertension among persons living with HIV-Zambia, 2021; A cross-sectional study of a national electronic health record system.

Hypertension is a major risk factor for cardiovascular disease, which is a common cause of death in Zambia. Data on hypertension prevalence in Zambia are scarce and limited to specific geographic areas and/or populations. We measured hypertension prevalence among persons living with HIV (PLHIV) in Zambia using a national electronic health record (EHR) system. We did a cross-sectional study of hypertension prevalence among PLHIV aged ≥18 years during 2021. Data were extracted from the SmartCare EHR, which covers ~90% of PLHIV on treatment in Zambia. PLHIV with ≥2 clinical visits in 2021 were included. Hypertension was defined as ≥2 elevated blood pressure readings (systolic ≥140 mmHg/diastolic ≥90 mmHg) during 2021 and/or on anti-hypertensive medication recorded in their EHR ≤5 years. Logistic regression was used to assess for associations between hypertension and demographic characteristics. Among 750,098 PLHIV aged ≥18 years with ≥2 visits during 2021, 101,363 (13.5%) had ≥2 recorded blood pressure readings. Among these PLHIV, 14.7% (95% confidence interval [CI]: 14.5-14.9) had hypertension. Only 8.9% of PLHIV with hypertension had an anti-hypertensive medication recorded in their EHR. The odds of hypertension were greater in older age groups compared to PLHIV aged 18-29 years (adjusted odds ratio [aOR] for 30-44 years: 2.6 [95% CI: 2.4-2.9]; aOR for 45-49 years: 6.4 [95% CI: 5.8-7.0]; aOR for ≥60 years: 14.5 [95% CI: 13.1-16.1]), urban areas (aOR: 1.9 [95% CI: 1.8-2.1]), and on ART for ≥6-month at a time (aOR: 1.1 [95% CI: 1.0-1.2]). Hypertension was common among PLHIV in Zambia, with few having documentation of treatment. Most PLHIV were excluded from the analysis because of missing BP measurements. Strengthening integrated management of non-communicable diseases in HIV clinics might help to diagnose and treat hypertension in Zambia. Addressing missing data of routine clinical data (like blood pressure) could improve non-communicable diseases surveillance in Zambia.

Defining Opioid-related Problems Using a Health Care Safety Net Institution's Inpatient Electronic Health Records: Limitations of Diagnosis-based Definitions.

BACKGROUND: Measuring clinically relevant opioid-related problems in health care systems is challenging due to the lack of standard definitions and coding practices. Well-defined, opioid-related health problems (ORHPs) would improve prevalence estimates and evaluation of clinical interventions, crisis response, and prevention activities. We sought to estimate prevalence of opioid use disorder (OUD), opioid misuse, and opioid poisoning among inpatients at a large, safety net, health care institution. METHODS: Our study included events documented in the electronic health records (EHRs) among hospitalized patients at Denver Health Medical Center during January 1, 2017 to December 31, 2018. Multiple EHR markers (ie, opioid-related diagnostic codes, clinical assessment, laboratory results, and free-text documentation) were used to develop diagnosis-based and extended definitions for OUD, opioid misuse, and opioid poisoning. We used these definitions to estimate number of hospitalized patients with these conditions. RESULTS: During a 2-year study period, 715 unique patients were identified solely using opioid-related diagnostic codes; OUD codes accounted for the largest proportion (499/715, 69.8%). Extended definitions identified an additional 973 unique patients (~136% increase), which includes 155/973 (15.9%) who were identified by a clinical assessment marker, 1/973 (0.1%) by a laboratory test marker, and 817/973 (84.0%) by a clinical documentation marker. CONCLUSIONS: Solely using diagnostic codes to estimate prevalence of clinically relevant ORHPs missed most patients with ORHPs. More inclusive estimates were generated using additional EHR markers. Improved methods to estimate ORHPs among a health care system's patients would more fully estimate organizational and economic burden to more efficiently allocate resources and ensure capacity to provide clinical services.

Measuring Oral Pre-exposure Prophylaxis (PrEP) Continuation Through Electronic Health Records During Program Scale-Up Among the General Population in Zambia.

HIV pre-exposure prophylaxis (PrEP) is being scaled-up in Zambia, but PrEP continuation data are limited by paper-based registers and aggregate reports. Utilization of Zambia's electronic health record (EHR) system, SmartCare, may address this gap. We analyzed individuals aged ≥ 15 years who initiated PrEP between October 2020 and September 2021 in four provinces in Zambia in SmartCare versus aggregate reports. We measured PrEP continuation using Kaplan-Meier survival analysis and Cox proportional hazards models. SmartCare captured 29% (16,791/58,010) of new PrEP clients; 49% of clients continued at one month, and 89% discontinued PrEP by February 2022. Women were less likely than men to discontinue PrEP (adjusted hazard ratio [aHR]: 0.89, 95% CI 0.86-0.92, z = - 6.99, p < 0.001), and PrEP clients aged ≥ 50 years were less likely to discontinue PrEP compared to clients 15-19 years (aHR: 0.53, 95% CI 0.48-0.58, z = - 13.04, p < 0.001). Zambia's EHR is a valuable resource for measuring individual-level PrEP continuation over time and can be used to inform HIV prevention programs.

The Detection of Opioid Misuse and Heroin Use From Paramedic Response Documentation: Machine Learning for Improved Surveillance.

BACKGROUND: Timely, precise, and localized surveillance of nonfatal events is needed to improve response and prevention of opioid-related problems in an evolving opioid crisis in the United States. Records of naloxone administration found in prehospital emergency medical services (EMS) data have helped estimate opioid overdose incidence, including nonhospital, field-treated cases. However, as naloxone is often used by EMS personnel in unconsciousness of unknown cause, attributing naloxone administration to opioid misuse and heroin use (OM) may misclassify events. Better methods are needed to identify OM. OBJECTIVE: This study aimed to develop and test a natural language processing method that would improve identification of potential OM from paramedic documentation. METHODS: First, we searched Denver Health paramedic trip reports from August 2017 to April 2018 for keywords naloxone, heroin, and both combined, and we reviewed narratives of identified reports to determine whether they constituted true cases of OM. Then, we used this human classification as reference standard and trained 4 machine learning models (random forest, k-nearest neighbors, support vector machines, and L1-regularized logistic regression). We selected the algorithm that produced the highest area under the receiver operating curve (AUC) for model assessment. Finally, we compared positive predictive value (PPV) of the highest performing machine learning algorithm with PPV of searches of keywords naloxone, heroin, and combination of both in the binary classification of OM in unseen September 2018 data. RESULTS: In total, 54,359 trip reports were filed from August 2017 to April 2018. Approximately 1.09% (594/54,359) indicated naloxone administration. Among trip reports with reviewer agreement regarding OM in the narrative, 57.6% (292/516) were considered to include information revealing OM. Approximately 1.63% (884/54,359) of all trip reports mentioned heroin in the narrative. Among trip reports with reviewer agreement, 95.5% (784/821) were considered to include information revealing OM. Combined results accounted for 2.39% (1298/54,359) of trip reports. Among trip reports with reviewer agreement, 77.79% (907/1166) were considered to include information consistent with OM. The reference standard used to train and test machine learning models included details of 1166 trip reports. L1-regularized logistic regression was the highest performing algorithm (AUC=0.94; 95% CI 0.91-0.97) in identifying OM. Tested on 5983 unseen reports from September 2018, the keyword naloxone inaccurately identified and underestimated probable OM trip report cases (63 cases; PPV=0.68). The keyword heroin yielded more cases with improved performance (129 cases; PPV=0.99). Combined keyword and L1-regularized logistic regression classifier further improved performance (146 cases; PPV=0.99). CONCLUSIONS: A machine learning application enhanced the effectiveness of finding OM among documented paramedic field responses. This approach to refining OM surveillance may lead to improved first-responder and public health responses toward prevention of overdoses and other opioid-related problems in US communities.

Monitoring opioid addiction and treatment: Do you know if your population is engaged?

BACKGROUND: Assessment of people affected by opioid-related problems and those receiving care is challenging due to lack of common definitions and scattered information. We sought to fill this gap by demonstrating a method to describe a continuum of opioid addiction care in a large, public safety-net institution. METHODS: Using 2017 clinical and administrative data from Denver Health (DH), we created operational definitions for opioid use disorder (OUD), opioid misuse (OM), and opioid poisoning (OP). Six stages along a continuum of patient engagement in opioid addiction care were developed, and operational definitions assigned patients to stages for a specific time point of analysis. National data was used to estimate the Denver population affected by OUD, OM and OP. RESULTS: In 2017, an estimated 6688 people aged ≥12 years were affected by OUD, OM, or OP in Denver; 48.4% (3238/6688) were medically diagnosed in DH. Of those, 32.5% (1051/3238) were in the medication assisted treatment stage, and, of those, 59.8% (629/1051) in the adhered to treatment stage. Among that latter group, 78.4% (493/629) adhered at least 90 days and 52.3% (329/629) for more than one year. Among patients who received medication assisted treatment, less than one third (31.3%, 329/1051) were adherent for more than one year. CONCLUSIONS: A health-system level view of the continuum of opioid addiction care identified improvement opportunities to better monitor accuracy of diagnosis, treatment capacity, and effectiveness of patient engagement. Applied longitudinally at local, state and national levels, the model could better synergize responses to the opioid crisis.

Will Participatory Syndromic Surveillance Work in Latin America? Piloting a Mobile Approach to Crowdsource Influenza-Like Illness Data in Guatemala.

BACKGROUND: In many Latin American countries, official influenza reports are neither timely nor complete, and surveillance of influenza-like illness (ILI) remains thin in consistency and precision. Public participation with mobile technology may offer new ways of identifying nonmedically attended cases and reduce reporting delays, but no published studies to date have assessed the viability of ILI surveillance with mobile tools in Latin America. We implemented and assessed an ILI-tailored mobile health (mHealth) participatory reporting system. OBJECTIVE: The objectives of this study were to evaluate the quality and characteristics of electronically collected data, the user acceptability of the symptom reporting platform, and the costs of running the system and of identifying ILI cases, and to use the collected data to characterize cases of reported ILI. METHODS: We recruited the heads of 189 households comprising 584 persons during randomly selected home visits in Guatemala. From August 2016 to March 2017, participants used text messages or an app to report symptoms of ILI at home, the ages of the ILI cases, if medical attention was sought, and if medicines were bought in pharmacies. We sent weekly reminders to participants and compensated those who sent reports with phone credit. We assessed the simplicity, flexibility, acceptability, stability, timeliness, and data quality of the system. RESULTS: Nearly half of the participants (47.1%, 89/189) sent one or more reports. We received 468 reports, 83.5% (391/468) via text message and 16.4% (77/468) via app. Nine-tenths of the reports (93.6%, 438/468) were received within 48 hours of the transmission of reminders. Over a quarter of the reports (26.5%, 124/468) indicated that at least someone at home had ILI symptoms. We identified 202 ILI cases and collected age information from almost three-fifths (58.4%, 118/202): 20 were aged between 0 and 5 years, 95 were aged between 6 and 64 years, and three were aged 65 years or older. Medications were purchased from pharmacies, without medical consultation, in 33.1% (41/124) of reported cases. Medical attention was sought in 27.4% (34/124) of reported cases. The cost of identifying an ILI case was US $6.00. We found a positive correlation (Pearson correlation coefficient=.8) between reported ILI and official surveillance data for noninfluenza viruses from weeks 41 (2016) to 13 (2017). CONCLUSIONS: Our system has the potential to serve as a practical complement to respiratory virus surveillance in Guatemala. Its strongest attributes are simplicity, flexibility, and timeliness. The biggest challenge was low enrollment caused by people's fear of victimization and lack of phone credit. Authorities in Central America could test similar methods to improve the timeliness, and extend the breadth, of disease surveillance. It may allow them to rapidly detect localized or unusual circulation of acute respiratory illness and trigger appropriate public health actions.

Modeling and testing maternal and newborn care mHealth interventions: a pilot impact evaluation and follow-up qualitative study in Guatemala.

Objective: To develop a descriptive model of structural characteristics of mHealth in the context of newborn nutrition, and to assess the effects of illustrative interventions through a mixed-methods study consisting of an impact evaluation and a qualitative assessment. Materials and Methods: We conducted a 23-week intervention with 100 mothers in rural Guatemala in 2013 and 2014. In group 1 ( n = 24), participants received health-promoting text messages. In group 2 ( n = 32), peer-to-peer groups were formed. In group 3 ( n = 30), peer-to-peer groups were formed, a health professional participated in the discussions, and participants received health-promoting messages. In the control group ( n = 14), participants were simply given a mobile phone. We measured changes in knowledge and self-reported behavior. Four focus groups in 2015 showed the perceptions of 44 additional women and the potential of the previously tested interventions in other marginalized areas. Results: Significant relationships were found between group membership and changes in knowledge ( P < .001), and between changes in knowledge and self-reported behavior ( P = .010). Within peer-to-peer groups, 3665 text messages were shared; discussions covered topics such as breastfeeding practices, health concerns, and emotional issues. Focus groups revealed a deficit of support for mothers, a precariousness of public services, different cultural barriers affecting access to care, and the potential for scaling up. Discussion: The complementarity of structural arrangements of mHealth interventions can play an important role in helping to encourage recommended breastfeeding attitudes along with providing rich information about challenges in rural areas. Conclusion: A mixed-methods study was appropriate to compare the effects and assess the potential of mHealth strategies in a complex rural setting.

Unaddressed privacy risks in accredited health and wellness apps: a cross-sectional systematic assessment.

BACKGROUND: Poor information privacy practices have been identified in health apps. Medical app accreditation programs offer a mechanism for assuring the quality of apps; however, little is known about their ability to control information privacy risks. We aimed to assess the extent to which already-certified apps complied with data protection principles mandated by the largest national accreditation program. METHODS: Cross-sectional, systematic, 6-month assessment of 79 apps certified as clinically safe and trustworthy by the UK NHS Health Apps Library. Protocol-based testing was used to characterize personal information collection, local-device storage and information transmission. Observed information handling practices were compared against privacy policy commitments. RESULTS: The study revealed that 89% (n = 70/79) of apps transmitted information to online services. No app encrypted personal information stored locally. Furthermore, 66% (23/35) of apps sending identifying information over the Internet did not use encryption and 20% (7/35) did not have a privacy policy. Overall, 67% (53/79) of apps had some form of privacy policy. No app collected or transmitted information that a policy explicitly stated it would not; however, 78% (38/49) of information-transmitting apps with a policy did not describe the nature of personal information included in transmissions. Four apps sent both identifying and health information without encryption. Although the study was not designed to examine data handling after transmission to online services, security problems appeared to place users at risk of data theft in two cases. CONCLUSIONS: Systematic gaps in compliance with data protection principles in accredited health apps question whether certification programs relying substantially on developer disclosures can provide a trusted resource for patients and clinicians. Accreditation programs should, as a minimum, provide consistent and reliable warnings about possible threats and, ideally, require publishers to rectify vulnerabilities before apps are released.

Smartphone apps for calculating insulin dose: a systematic assessment.

BACKGROUND: Medical apps are widely available, increasingly used by patients and clinicians, and are being actively promoted for use in routine care. However, there is little systematic evidence exploring possible risks associated with apps intended for patient use. Because self-medication errors are a recognized source of avoidable harm, apps that affect medication use, such as dose calculators, deserve particular scrutiny. We explored the accuracy and clinical suitability of apps for calculating medication doses, focusing on insulin calculators for patients with diabetes as a representative use for a prevalent long-term condition. METHODS: We performed a systematic assessment of all English-language rapid/short-acting insulin dose calculators available for iOS and Android. RESULTS: Searches identified 46 calculators that performed simple mathematical operations using planned carbohydrate intake and measured blood glucose. While 59% (n = 27/46) of apps included a clinical disclaimer, only 30% (n = 14/46) documented the calculation formula. 91% (n = 42/46) lacked numeric input validation, 59% (n = 27/46) allowed calculation when one or more values were missing, 48% (n = 22/46) used ambiguous terminology, 9% (n = 4/46) did not use adequate numeric precision and 4% (n = 2/46) did not store parameters faithfully. 67% (n = 31/46) of apps carried a risk of inappropriate output dose recommendation that either violated basic clinical assumptions (48%, n = 22/46) or did not match a stated formula (14%, n = 3/21) or correctly update in response to changing user inputs (37%, n = 17/46). Only one app, for iOS, was issue-free according to our criteria. No significant differences were observed in issue prevalence by payment model or platform. CONCLUSIONS: The majority of insulin dose calculator apps provide no protection against, and may actively contribute to, incorrect or inappropriate dose recommendations that put current users at risk of both catastrophic overdose and more subtle harms resulting from suboptimal glucose control. Healthcare professionals should exercise substantial caution in recommending unregulated dose calculators to patients and address app safety as part of self-management education. The prevalence of errors attributable to incorrect interpretation of medical principles underlines the importance of clinical input during app design. Systemic issues affecting the safety and suitability of higher-risk apps may require coordinated surveillance and action at national and international levels involving regulators, health agencies and app stores.